Zhang, Jun, Zhou, Xiaoyi ORCID: https://orcid.org/0000-0003-3777-9479, Yang, Jilin, Cao, Chunjie ORCID: https://orcid.org/0000-0001-9439-8256 and Ma, Jixin (2019) Adaptive robust blind watermarking scheme improved by entropy-based SVM and optimized quantum genetic algorithm. Mathematical Problems in Engineering, 2019:7817809. pp. 1-16. ISSN 1024-123X (Print), 1563-5147 (Online) (doi:10.1155/2019/7817809)

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Abstract

With the intensive study of machine learning in digital watermarking, its ability to balance the robustness and transparency of watermarking technology has attracted researchers’ attention. Therefore, quantum genetic algorithm, which serves as an intelligent optimized scheme combined with biological genetic mechanism and quantum computing, is widely used in various fields. In this study, an adaptive robust blind watermarking algorithm by means of optimized quantum genetics (OQGA) and entropy classification-based SVM (support vector machine) is proposed. The host image was divided into two parts according to the odd and even rows of the host image. One part was transformed by DCT (discrete cosine transform), and then the embedding intensity and position were separately trained by entropy-based SVM and OQGA; the other part was by DWT (discrete wavelet transform), in which the key fusion was achieved by an ergodic matrix to embed the watermark. Simulation results indicate the proposed algorithm ensures the watermark scheme transparency as well as having better resistance to common attacks such as lossy JPEG compression, image darken, Gaussian low-pass filtering, contrast decreasing, salt-pepper noise, and geometric attacks such as rotation and cropping.

Received
22 May 2019
Revised
18 Aug 2019
Accepted
17 Sep 2019
Published
28 Oct 2019

Item Type:	Article
Uncontrolled Keywords:	blind watermarking scheme; entropy-based support vector machine; optimized quantum genetic algorithm
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Related URLs:	Publisher
Last Modified:	19 May 2022 11:29
URI:	http://gala.gre.ac.uk/id/eprint/36273

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